A hidden Markov model with abnormal states for detecting stock price manipulation

Cao, Yi, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Coleman, Sonya, Belatreche, Ammar and McGinnity, T. M. 2014. A hidden Markov model with abnormal states for detecting stock price manipulation. Presented at: SMC 2013, Manchester, UK, 13-16 Oct 2013. 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics. Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics Piscataway, NJ: IEEE, pp. 3014-3019. 10.1109/SMC.2013.514

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Abstract

Price manipulation refers to the act of using illegal trading behaviour to manually change an equity price with the aim of making profits. With increasing volumes of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. Effective approaches for analysing and real-time detection of price manipulation are yet to be developed. This paper proposes a novel approach, called Hidden Markov Model with Abnormal States (HMMAS), which models and detects price manipulation activities. Together with the wavelet decomposition for features extraction and Gaussian Mixture Model for Probability Density Function (PDF) construction, the HMMAS model detects price manipulation and identifies the type of the detected manipulation. Evaluation experiments of the model were conducted on six stock tick data from NASDAQ and London Stock Exchange (LSE). The results showed that the proposed HMMAS model can effectively detect price manipulation patterns.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-4799-0650-5
ISSN:	1062-922X
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129139

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